Review-based Entity-ranking Refinement

Panagiotis Gourgaris, Andreas Kanavos, Christos Makris, Georgios Perrakis


In this paper, we address the problem of entity ranking using opinions expressed in users’ reviews. There is an abundance of opinions on the web, which includes reviews of products and services. Specifically, we examine techniques which utilize clustering information, for coping with the obstacle of the entity ranking problem. Building on this framework, we propose a probabilistic network scheme that employs a topic identification method so as to modify ranking of results based on user personalization. The contribution lies in the construction of a probabilistic network which takes as input the belief of the user for each query (initially, all entities are equivalent) and produces a new ranking for the entities as output. We evaluated our implemented methodology with experiments with the OpinRank Dataset where we observed an improved retrieval performance to current re-ranking methods.


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Paper Citation

in Harvard Style

Gourgaris P., Kanavos A., Makris C. and Perrakis G. (2015). Review-based Entity-ranking Refinement . In Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-106-9, pages 402-410. DOI: 10.5220/0005428604020410

in Bibtex Style

author={Panagiotis Gourgaris and Andreas Kanavos and Christos Makris and Georgios Perrakis},
title={Review-based Entity-ranking Refinement},
booktitle={Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Review-based Entity-ranking Refinement
SN - 978-989-758-106-9
AU - Gourgaris P.
AU - Kanavos A.
AU - Makris C.
AU - Perrakis G.
PY - 2015
SP - 402
EP - 410
DO - 10.5220/0005428604020410